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Blastic plasmacytoid dendritic cell neoplasia (BPDCN) is a rare myeloid malignancy with a generally poor prognosis. Although preliminary evidence suggests that hematopoietic cell transplantation (HCT) could improve outcome in patients with BPDCN, the individual contributions of conditioning and graft-versus-tumor (GVT) effects to HCT success are undefined. We present a retrospective study of 162 adult patients who underwent a first HCT (allogeneic 146, autologous 16) between 2009 and 2017, and were registered with the EBMT. Median age was 57 (range 20–73) years, and disease status at HCT was first complete remission (CR1) in 78%. Among patients receiving allogeneic HCT (alloHCT), myeloablative conditioning (MAC), reduced intensity conditioning (RIC) and in-vivo T-cell depletion (TCD) were used in 54%, 46%, and 59% respectively. Total body irradiation (TBI) was the conditioning backbone in 61% of MAC and 26% of RIC transplants. One-year overall survival (OS) and progression-free survival (PFS) rates were comparable after alloHCT and autologous HCT (autoHCT). Among alloHCT recipients, MAC with TBI significantly improved OS and PFS, independently of CR1, age, Karnofsky index and TCD. Accordingly, MAC (ideally based on TBI) should be preferred for alloHCT recipients with BPDCN. In patients who are not elegible for MAC alloHCT, autoHCT could be considered.

Three reports address the protection of the vulnerable population of patients with hematologic malignancies in the face of the ongoing COVID pandemic. The reports suggest that some patients who fail to mount a B-cell response to vaccine may nevertheless have protective T cell responses. As a group, these reports suggest that patients should continue to be immunized with additional doses to attempt to improve immune response but that they need to maintain the precautions recommended for the unvaccinated.

Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We present a computational method, scNOVA, which uses Strand-seq to perform haplotype-aware integration of SV discovery and molecular phenotyping in single cells by using nucleosome occupancy to infer gene expression as a readout. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of SVs on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. By directly linking SVs to their functional effects, scNOVA enables systematic single-cell multiomic studies of structural variation in heterogeneous cell populations.

SummaryT-cell-engaging immunotherapies have improved the treatment of nodal B-cell lymphoma, but responses vary highly. Future improvements of such therapies require better understanding of the variety of lymphoma-infiltrating T-cells. We employed single-cell RNA and T-cell receptor sequencing alongside quantification of surface proteins, flow cytometry and multiplexed immunofluorescence on 101 lymph nodes from healthy controls, and patients with diffuse large B-cell, mantle cell, follicular, or marginal zone lymphoma. This multimodal resource revealed entity-specific quantitative and spatial aberrations of the T-cell microenvironment. Clonal PD1+TCF7-but not PD1+TCF7+cytotoxic T-cells converged into terminally exhausted T-cells, the proportions of which were variable across entities and linked to inferior prognosis. In follicular and marginal zone lymphoma, we observed expansion of follicular helper and IKZF3+regulatory T-cells, which were clonally related and inversely associated with tumor grading. Overall, we portray lymphoma-infiltrating T-cells with unprecedented comprehensiveness and decipher both beneficial and adverse dimensions of T-cell response.

Chronic lymphocytic leukemia (CLL) is a B-cell malignancy mainly occurring at an advanced age with no single major genetic driver. Transgenic expression of TCL1 in B cells leads after a long latency to a CLL-like disease in aged Eµ-TCL1 mice suggesting that TCL1 overexpression is not sufficient for full leukemic transformation. In search for secondary genetic events and to elucidate the clonal evolution of CLL, we performed whole exome and B-cell receptor sequencing of longitudinal leukemia samples of Eµ-TCL1 mice. We observed a B-cell receptor stereotypy, as described in patients, confirming that CLL is an antigen-driven disease. Deep sequencing showed that leukemia in Eµ-TCL1 mice is mostly monoclonal. Rare oligoclonality was associated with inability of tumors to develop disease upon adoptive transfer in mice. In addition, we identified clonal changes and a sequential acquisition of mutations with known relevance in CLL, which highlights the genetic similarities and therefore, suitability of the Eµ-TCL1 mouse model for progressive CLL. Among them, a recurrent gain of chromosome 15, where Myc is located, was identified in almost all tumors in Eµ-TCL1 mice. Interestingly, amplification of 8q24, the chromosomal region containing MYC in humans, was associated with worse outcome of patients with CLL., Clonal evolution and genomic alterations in the Eµ-TCL1 mouse model mirror human CLL and identify Myc as oncogenic hit associated with disease progression in patients.

The tumour microenvironment and genetic alterations collectively influence drug efficacy in cancer, but current evidence is limited and systematic analyses are lacking. Using chronic lymphocytic leukaemia (CLL) as a model disease, we investigated the influence of 17 microenvironmental stimuli on 12 drugs in 192 genetically characterised patient samples. Based on microenvironmental response, we identified four subgroups with distinct clinical outcomes beyond known prognostic markers. Response to multiple microenvironmental stimuli was amplified in trisomy 12 samples. Trisomy 12 was associated with a distinct epigenetic signature. Bromodomain inhibition reversed this epigenetic profile and could be used to target microenvironmental signalling in trisomy 12 CLL. We quantified the impact of microenvironmental stimuli on drug response and their dependence on genetic alterations, identifying interleukin 4 (IL4) and Toll-like receptor (TLR) stimulation as the strongest actuators of drug resistance. IL4 and TLR signalling activity was increased in CLL-infiltrated lymph nodes compared with healthy samples. High IL4 activity correlated with faster disease progression. The publicly available dataset can facilitate the investigation of cell-extrinsic mechanisms of drug resistance and disease progression.

Chronic Lymphocytic Leukemia (CLL) has a complex pattern of driver mutations and much of its clinical diversity remains unexplained. We devised a method for simultaneous subgroup discovery across multiple data types and applied it to genomic, transcriptomic, DNA methylation and ex-vivo drug response data from 217 Chronic Lymphocytic Leukemia (CLL) cases. We uncovered a biological axis of heterogeneity strongly associated with clinical behavior and orthogonal to the known biomarkers. We validated its presence and clinical relevance in four independent cohorts (n=547 patients). We find that this axis captures the proliferative drive (PD) of CLL cells, as it associates with lymphocyte doubling rate, global hypomethylation, accumulation of driver aberrations and response to pro-proliferative stimuli. CLL-PD was linked to the activation of mTOR-MYC-oxidative phosphorylation (OXPHOS) through transcriptomic, proteomic and single cell resolution analysis. CLL-PD is a key determinant of disease outcome in CLL. Our multi-table integration approach may be applicable to other tumors whose inter-individual differences are currently unexplained.

The tumour microenvironment and genetic alterations collectively influence drug efficacy in cancer, but current evidence is limited to small scale studies and systematic analyses are lacking. We chose Chronic Lymphocytic Leukaemia (CLL), the most common leukaemia in adults, as a model disease to study this complex interplay systematically. We performed a combinatorial assay using 12 drugs individually co-applied with each of 17 microenvironmental stimuli in 192 primary CLL samples, generating a comprehensive map of drug-microenvironment interactions in CLL. This data was combined with whole-exome sequencing, DNA-methylation, RNA-sequencing and copy number variant annotation. Our assay identified four distinct CLL subgroups that differed in their responses to the panel of microenvironmental stimuli. These subgroups were characterized by distinct clinical outcomes independently of known prognostic markers. We investigated the effect of CLL- specific recurrent genetic alterations on microenvironmental responses and identified trisomy 12 as an amplifier of multiple microenvironmental stimuli. We further quantified the impact of microenvironmental stimuli on drug response, confirmed known interactions such as Interleukin (IL) 4 mediated resistance to B cell receptor (BCR) inhibitors, and identified new interactions such as Interferon-γ induced resistance to BCR inhibitors. Finally, we identified interactions which were limited to genetic subgroups. Resistance to chemotherapeutics, such as Fludarabine, induced by Toll-Like Receptor (TLR) agonists could be observed in IGHV unmutated patient samples and IGHV mutated samples with trisomy 12. In-vivo relevance was investigated in CLL-infiltrated lymph nodes, which showed increased IL4 and TLR signalling activity compared to healthy samples (pWe provide a publicly available resource (www.dietrichlab.de/CLL_Microenvironment/) which uncovers tumour cell extrinsic influences on drug response and disease progression in CLL, and how these interactions are modulated by cell intrinsic molecular features.

Somatic structural variants (SVs) are widespread in cancer genomes, however, their impact on tumorigenesis and intra-tumour heterogeneity is incompletely understood, since methods to functionally characterize the broad spectrum of SVs arising in cancerous single-cells are lacking. We present a computational method, scNOVA, that couples SV discovery with nucleosome occupancy analysis by haplotype-resolved single-cell sequencing, to systematically uncover SV effects on cis-regulatory elements and gene activity. Application to leukemias and cell lines uncovered SV outcomes at several loci, including dysregulated cancer-related pathways and mono-allelic oncogene expression near SV breakpoints. At the intra-patient level, we identified different yet overlapping subclonal SVs that converge on aberrant Wnt signaling. We also deconvoluted the effects of catastrophic chromosomal rearrangements resulting in oncogenic transcription factor dysregulation. scNOVA directly links SVs to their functional consequences, opening the door for single-cell multiomics of SVs in heterogeneous cell populations.

Tumor heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is well-known to affect the efficacy of anti-cancer drugs, most personalized treatment approaches do not account for intratumor heterogeneity. We addressed this issue by studying the heterogeneity of lymph node-derived B cell non-Hodgkin lymphoma (B-NHL) by single cell RNA-sequencing (scRNA-seq) and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subclones and compared their drug response and genomic profiles. Malignant subclones of the same patient responded strikingly different to anti-cancer drugs ex vivo, which recapitulated subclone-specific drug sensitivity during in vivo treatment. Tumor infiltrating T cells represented the majority of non-malignant cells, whose gene expression signatures were similar across all donors, whereas the frequencies of T cell subsets varied significantly between the donors. Our data provide new insights into the heterogeneity of B-NHL and highlight the relevance of intratumor heterogeneity for personalized cancer therapies.

Summary: Transcription factors (TFs) regulate many cellular processes and can therefore serve as readouts of the signaling and regulatory state. Yet for many TFs, the mode of action—repressing or activating transcription of target genes—is unclear. Here, we present diffTF (https://git.embl.de/grp-zaugg/diffTF) to calculate differential TF activity (basic mode) and classify TFs into putative transcriptional activators or repressors (classification mode). In basic mode, it combines genome-wide chromatin accessibility/activity with putative TF binding sites that, in classification mode, are integrated with RNA-seq. We apply diffTF to compare (1) mutated and unmutated chronic lymphocytic leukemia patients and (2) two hematopoietic progenitor cell types. In both datasets, diffTF recovers most known biology and finds many previously unreported TFs. It classifies almost 40% of TFs based on their mode of action, which we validate experimentally. Overall, we demonstrate that diffTF recovers known biology, identifies less well-characterized TFs, and classifies TFs into transcriptional activators or repressors. : Berest et al. present a computational tool (diffTF) to estimate differential TF activity and classify TFs into activators or repressors. It requires active chromatin data (accessibility/ChIP-seq) and integrates with RNA-seq for classification. The authors apply it to two case studies (CLL and hematopoietic differentiation) and validate their predictions experimentally. Keywords: transcription factor, ATAC-seq, CLL, transcriptional activator and repressor, RNA-seq, TF footprint, open chromatin, snakemake, multiomics data integration

Background: Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in the western world and shows a very heterogeneous clinical course. While the genetic landscape of CLL has been well characterized during recent years it can only partially explain the underlying biology of this heterogeneity. Proteogenomics could offer a valuable tool to fill this gap and improve the understanding of CLL biology. Methods: Here, we performed a large proteogenomic analysis (n=263) of three clinically annotated CLL cohorts: For the discovery cohort (Germany_1: n=68) we performed in-depth HiRIEF LC-MS based proteomics (more than 9000 proteins quantified) alongside genome-, transcriptome and ex-vivo drug response-profiling with 43 clinically established drugs. The proteome of two additional validation cohorts (Germany_2: n=44, Sweden_1: n=89), were characterized by data-independent acquisition (DIA) mass spectrometry. Results: To connect the CLL genotype with the molecular phenotype, we investigated associations between recurrent genetic alterations of CLL, mRNA expression and protein abundance. We found that trisomy 12, IGHV status and SF3B1 mutations had the greatest impact on protein abundances. CLL specific recurrent chromosomal deletions and gains (trisomy 12, del17p, del13q, del11q, gain8q24) consistently impacted on gene expression and protein abundance through gene dosage effects. We explored functional consequences of these gene dosage effects and found that the additional copy of chromosome 12 increased the abundance of central B-cell receptor (BCR) protein complexes through cis- and trans-effects, which could explain the increased response of trisomy 12 patient samples to BCR inhibition. Somatic mutations of TP53, ATM and XPO1 were associated with less, but specific and biologically-relevant protein abundance changes. p53 for instance, was the most upregulated protein in TP53 mutated samples, owing to the known stabilisation of mutant p53. This effect was not detectable on transcript level. ATM and XPO1 protein abundances were significantly lower in ATM and XPO1 mutated cases, indicating loss-of-function phenotypes of these mutations. To understand global similarities and differences between CLL patients on the proteomic level, we performed unsupervised clustering and identified clinically meaningful subgroups. Unsupervised clustering of the proteomics data identified six subgroups with contrasting clinical behaviour. TP53 mutations, IGHV status, trisomy 12 and their interactions explained five subgroups. These results show that quantitative mass spectrometry-based proteomics distinguished clinically relevant subgroups of CLL. Most importantly, we identified a previously unappreciated subgroup of CLL, comprising 20% of all cases, which could be uncovered by proteomic profiling and showed no association with frequent genetic or transcriptional alterations. This new CLL subgroup was characterized by accelerated disease progression, SF3B1 mutation-independent splicing alterations, metabolomic reprogramming and increased vulnerability to inhibitors of metabolic enzymes and the proteasome. Surprisingly, major BCR signaling proteins were downregulated in this subgroup, suggesting less dependence on BCR activity. In accordance with this observation, an unsupervised analysis revealed that low levels of many BCR signaling proteins (e.g. PLCG2 and PIK3CD) were associated with short time to next treatment. The existence of this subgroup could be confirmed in the validation cohorts. Finally, we performed an unsupervised multi-omics factor analysis (MOFA) across all omics data sets in parallel. This unsupervised analysis confirmed the existence of the above identified CLL subgroups and an important role of SF3B1 mutation-independent splicing alterations in CLL. Conclusion: Our integrative multi-omics analysis provides the first comprehensive overview of the interplay between genetic variants, the transcriptome, and the proteome, along with functional consequences for drug response and clinical outcome in CLL. Importantly, we identified a new subgroup with accelerated disease progression, a distinct proteomic signature and a clinically exploitable drug sensitivity profile. Figure Disclosures Mueller-Tidow: BiolineRx: Research Funding; Daiichi Sankyo: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMBF: Research Funding; Wilhelm-Sander-Stiftung: Research Funding; Jose-Carreras-Siftung: Research Funding; Bayer AG: Research Funding; Deutsche Krebshilfe: Research Funding; Deutsche Forschungsgemeinschaft: Research Funding; Janssen-Cilag Gmbh: Membership on an entity's Board of Directors or advisory committees. Dreger:Neovii: Research Funding; Roche: Consultancy, Speakers Bureau; Riemser: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Janssen: Consultancy; Gilead: Consultancy, Speakers Bureau; AstraZeneca: Consultancy; AbbVie: Consultancy, Speakers Bureau. Stilgenbauer:Pharmacyclics: Consultancy, Honoraria, Other, Research Funding; Novartis: Consultancy, Honoraria, Other, Research Funding; Mundipharma: Consultancy, Honoraria, Other, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Other: travel support, Research Funding; Gilead: Consultancy, Honoraria, Other: travel support, Research Funding; Genzyme: Consultancy, Honoraria, Other: travel support, Research Funding; Genentech: Consultancy, Honoraria, Other: travel support, Research Funding; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding; Celgene: Consultancy, Honoraria, Other: travel support, Research Funding; Boehringer-Ingelheim: Consultancy, Honoraria, Other: travel support, Research Funding; Amgen: Consultancy, Honoraria, Other: travel support, Research Funding; AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding. Tausch:Roche: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Research Funding. Dietrich:Roche: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees.

Tumour heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is known to affect the efficacy of cancer therapy, most personalized treatment approaches do not account for intratumour heterogeneity. We addressed this issue by studying the heterogeneity of nodal B-cell lymphomas by single-cell RNA-sequencing and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subpopulations and compared their drug-response and genomic profiles. Malignant subpopulations from the same patient responded strikingly differently to anti-cancer drugs ex vivo, which recapitulated subpopulation-specific drug sensitivity during in vivo treatment. Infiltrating T cells represented the majority of non-malignant cells, whose gene-expression signatures were similar across all donors, whereas the frequencies of T-cell subsets varied significantly between the donors. Our data provide insights into the heterogeneity of nodal B-cell lymphomas and highlight the relevance of intratumour heterogeneity for personalized cancer therapy.

Introduction and Methods To investigate the mechanisms by which microenvironmental signals induce treatment resistance in chronic lymphocytic leukemia (CLL) we conducted a high-throughput perturbation assay combining pairwise 15 clinically relevant drugs with 18 stimulations mimicking the tumor microenvironment. We combined this with multi-layer omics data using whole exome sequencing, genome-wide DNA-methylation profiles and RNA sequencing to investigate molecular determinants of microenvironmental drug resistance. In parallel we assessed the in-vivo relevance of investigated pathways in 100 CLL infiltrated- and 100 healthy lymph node (LN) biopsies by staining for stimulus specific downstream pathway components using immunohistochemistry (IHC). Results We initially clustered drugs and microenvironmental stimuli separately based on the similarity of their response profiles across all CLL samples. We found drug response profiles to be correlated closely for drugs targeting the same signaling-pathway, e.g. the B-cell receptor inhibitors Ibrutinib (BTK) and PRT062607 (Syk) (Pearson correlation coefficient: r=0.78, 95% CI: 0.72 - 0.83). In contrast, responses to microenvironmental stimuli were less correlated, indicating diverse effects are caused by the tumor microenvironment (e.g. Interleukin 4 (IL-4) vs. Interleukin 2, Pearson correlation coefficient: r=0.17, 95% CI: 0.03 - 0.31). Twelve of the 18 tested stimulations enhanced survival of CLL cells, whereas two reduced the viability of CLL cells in-vitro (comparisons yielded BH adjusted t-test p-values Ten of the 18 stimulation effects were modulated by genetic alterations in CLL cells, for 6 stimuli two or more response modulating genetic alterations were identified (FDR 10%). IGHV mutation status for instance modulated the effects of TLR 7/8/9 agonists as well as Interleukine-1β, CD40 ligand and TGF-β1. We will present a detailed analysis of how microenvironmental stimuli depend on specific genetic alterations in CLL. To understand how soluble stimuli interfere with drug response we searched for specific interactions between stimulations and drugs with the following linear model: Viability ~ drug-effect + stimulation-effect + drug-effect:stimulation-effect and tested for significance of the interaction term. This approach allowed us to dissect interactions independently of the individual effects. We discovered different types of drug-effect:stimulation-effect interactions: 1) The microenvironmental stimulus had a strong pro-survival effect on the baseline viability and interfered with the response to drugs (e.g. IL-4:Ibrutinib, p-value< 0.0001). 2) The microenvironmental stimulus had no major effect on baseline viability but significantly interfered with response to drugs (e.g. Interferon-γ:Ibrutinib, p-value: 0.014). 3) The microenvironmental stimulus and the drug exhibited no major individual effect on CLL cell survival, but together significantly increased CLL cell viability (e.g. Interferon-γ:Ralimetinib, p-value: < 0.0001). We will present a detailed analysis of microenvironmental stimuli and their influence on drug response. In the final step we investigated the biological relevance of selected microenvironmental stimuli in patient LN- and bone marrow biopsies by IHC. CLL infiltrated LN exhibited a higher signaling activity of the IL-4 and TLR pathway than non-neoplastic LN (t-test, p-values Conclusion We present a systematic investigation on the tumor microenvironment in CLL, the underlying molecular determinants as well its interference with drug response which will serve as a scientific resource and starting point for further mechanistical investigations. Figure Disclosures Kriegsmann: Celgene: Research Funding; Bristol-Myers Squibb: Research Funding; Sanofi: Research Funding; Morphosys: Research Funding. Müller-Tidow:MSD: Membership on an entity's Board of Directors or advisory committees.

Introduction Ex-vivo drug response profiling (DRP) represents a promising strategy to better understand the tumor's pathway dependencies, genotype-phenotype associations and the underlying molecular networks. However, little is known about the predictive value of drug response profiling and its utility for individualized treatment recommendations. Methods We report the first analysis of our ongoing prospective non-interventional SMARTrial (Systematic and Mechanism-based Approach to Rational Treatment Trial of Blood Cancer; ClinicalTrials.gov: NCT03488641). This trial aims to demonstrate the feasibility of high-throughput DRP in the clinical routine setting and the ability to predict in-vivo outcome by DRP. Ex-vivo responses to 107 different drugs including chemotherapeutic agents and FDA-approved or clinically investigated small molecules were assessed by an ATP-based assay after 48 hours of incubation. Results were processed by our customized analysis pipeline, enabling immediate reporting of DRP results. Patients with hematological malignancies and scheduled treatment were eligible if sufficient tumor cells were available from either lymph node biopsies, the bone marrow or peripheral blood. Results 58 patients out of the target study population of 80 patients have been recruited (AML, n = 24; B-cell lymphomas, n = 28; T-cell-lymphoma, n = 5; ALL, n = 1) as of July 24th, 2019. The median follow-up was 6 months. The primary endpoint, defined as proportion of successfully completed DRP tests within 7 days, is used to evaluate the feasibility of DRP in the clinical routine. In our study cohort, 93% of all recruited patients met this primary endpoint. We will describe our optimized workflow for drug testing and data analysis, which enables us to provide DRP reports in the clinical routine. First, we analyzed if the DRP reflected expected genotype-drug response associations and systematically regressed mutational data on DRP phenotypes. This analysis exemplarily revealed that FLT3-ITD-mutated AML cells with a mutation ratio > 0.8 respond significantly stronger to FLT3 inhibitors than FLT3-ITD-mutated AML cells with a mutation ratio < 0.8 or wildtype AML cells (Quizartinib, t-test, p = 0.022; Gilteritinib, t-test, p < 0.001), confirming that the assay can uncover clinically meaningful vulnerabilities of cancer cells. 89.3% of all evaluable patients received treatments covered by our ex-vivo DRP assay, which facilitates the correlation of in-vivo and ex-vivo drug response. The majority of patients were scheduled for chemotherapy treatment (65%) or had received chemotherapy before (32%). In-vivo responses after scheduled treatment were categorized as response (Resp), incomplete response (IResp) or progressive disease (PD). We compared ex-vivo drug responses between these different in-vivo response categories. Cancer cells of patients with PD were significantly less sensitive to ex-vivo treatment with chemotherapy (mean of all ex-vivo tested chemotherapeutics) than patients with Resp (t-test, PD vs. Resp, p = 0.007; PD vs. IResp, p = 0.054). While the anthracycline daunorubicin (t-test, p = 0.034) and the purin analogon fludarabine (t-test, p = 0.004) were significantly more active in samples from patients with a clinical Resp than in samples from patients with PD, we did not observe this trend for vinca alkaloids (e.g. vincristine, vindesine) or antifolates (e.g. pralatrexate). Interestingly, individual patients exhibited an exceptional ex-vivo sensitivity to anti-folates, despite an almost complete insensitivity to all other chemotherapeutic drugs. For a patient with refractory Burkitt Lymphoma, we observed such a strong ex-vivo sensitivity to pralatrexate despite a general ex-vivo insensitivity to most drugs. This patient had progressed on treatment with DA-EPOCH and DHAP, but responded well to methotrexate, enabling him to undergo consolidating allogeneic stem cell transplantation. Conclusion Ex-vivo drug response profiling prior to treatment can be successfully implemented in the clinical routine. Our preliminary results suggest that DRP can predict in-vivo treatment resistance and support treatment guidance for individualized treatment. Disclosures Dreger: AbbVie, Gilead, Novartis, Riemser, Roche: Speakers Bureau; Neovii, Riemser: Research Funding; MSD: Membership on an entity's Board of Directors or advisory committees, Other: Sponsoring of Symposia; AbbVie, AstraZeneca, Gilead, Janssen, Novartis, Riemser, Roche: Consultancy.

Background: Signals provided by the microenvironment can modify and circumvent pathway activities that are therapeutically targeted by drugs. However, a systems-level understanding of how the microenvironment and the genetic and molecular alterations of the tumor interact with each other and contribute to drug resistance is lacking. Methods: To address this unmet need, we established an automated microscopy-based phenotyping platform that uses co-culture conditions mimicking the bone marrow environment. We cultured primary tumor cells from more than 100 leukemia patients (CLL, AML, MCL, T-PLL, HCL) with and without bone marrow stroma cell support in DMEM and 10% human serum and treated each condition with 57 drugs in 3 concentrations. After 72h of incubation, 22 000 images per patient were acquired and processed by our custom made image analysis pipeline. Our set-up allows us to increase sensitivity far beyond simple viability testing, as it reads out additional cell type specific features such as cell morphology, autophagy and cell-cell interactions. Results: Quality assessment revealed that in contrast to mono-culture conditions, assay plate edge effects can be avoided under stable stroma cell co-culture conditions. Correlation of replicated patient samples were comparable between mono- and co-cultures (R2>0.75). In the absence of their native microenvironment, primary leukemia cells undergo spontaneous apoptosis ex-vivo. Viability at culture start was always >90% and dropped to a median of 51% (viability range: 17%-90%) after 72h in mono-cultures. Among CLL samples spontaneous apoptosis was not dependent on either IGHV mutation status or any major cytogenetic risk group. Bone marrow stroma cell co-culture conditions protected tumor cells from spontaneous apoptosis (p=8.2e-6, paired t-test). Patient samples with a high degree of spontaneous apoptosis benefited most from co-culture conditions (p=7.2e-10, Pearson correlation). To model interactions of stroma cell conditions and drug-induced apoptosis we established the following linear model: Viability ~ drug-effect + culture-model + drug-effect:culture-model. While activity of some drugs was significantly altered under co-culture conditions, we could also identify drugs with similar activity in mono- and co-cultures. For instance, the activity of common chemotherapeutics (fludarabine: p=0.002 at 0.6µM, cytarabine: p=0.001 at 1.5µM, ANOVA) or bromodomain inhibitors (I-BET-762: p=5.9e-5 at 4.5µM, JQ1: p=1.5e-8 at 1.5µM, ANOVA) was significantly reduced under co-culture conditions. In contrast, PI3K inhibitors idelalisib and duvelisib had a similar activity in mono-culture and stroma co-culture conditions and might represent a starting point to overcome stroma cell mediated drug resistance. In CLL, we identified IGHV mutation status and trisomy 12 as important determinants of response to kinase inhibitors. We confirmed these findings in stroma cell co-cultures, e.g. a better activity of B-cell receptor inhibitors in trisomy 12 and IGHV unmutated CLL. A systematic comparison of ex-vivo drug response pattern in mono- and co-cultures across 171 drug conditions will be presented. Conclusion: Our results suggest that high throughput co-culture drug testing can be robustly performed and provide an unprecedented understanding of how the stroma cell microenvironment and the genetic make-up of tumor cells contribute to drug resistance and sensitivity. Figure: Over 2 million microscopy images were acquired and analysed to assess drug resistance and sensitivity in a co-culture model of primary leukemia and bone marrow stroma cells. blue= Hoechst33342, green=Calcein AM, red=lysosomal dye NIR Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.